{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f3bd43c-acdf-408e-b16a-c3946dbf3a3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import font_manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a7a4c7-8073-485b-a7e7-d1e10592ff58",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 创建字体管理对象，指向具体的字体文件\n",
    "font = font_manager.FontProperties(\n",
    "    fname=\"/System/Library/Fonts/STHeiti Light.ttc\", size=14, weight=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddea7979-7f00-4b98-b068-9e1de97ec03b",
   "metadata": {
    "tags": []
   },
   "source": [
    "```python\n",
    "plt.boxplot(x, notch, sym, vert, whis, positions, ...)\n",
    "```\n",
    "### 参数解释\n",
    "* x: 需要绘制箱线图的数据\n",
    "* notch: 是否设置置信区间，默认为False\n",
    "* sym: 异常值的符号表示，默认为小圆点\n",
    "* vert: 是否设置垂直绘制，默认为True\n",
    "* whis: 设置上下限的系数，默认是1.5\n",
    "* positions: 绘制多个箱线时，每个箱线的位置\n",
    "* widths: 设置每个箱线的宽度\n",
    "* labels: 每个箱线的标签\n",
    "* meanline和showmeans: 是否绘制平均线，默认是False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9efb33-0d0f-46da-82b8-01350a79c371",
   "metadata": {},
   "source": [
    "## 一、单个箱线图的绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3920eb5c-8e41-4b52-b9ab-37c0d38e4eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  21.56028459,    6.85566119,   34.45984532,   76.6502239 ,\n",
       "         79.16351775,   14.495759  ,   79.13635878,   65.59841888,\n",
       "         55.9156918 ,   28.56281539,   15.79578538,   37.8298308 ,\n",
       "         77.23205445,   71.0311237 ,   64.5672139 ,   91.40509026,\n",
       "         41.22744501,   37.59515088,   67.91581516,   81.42502139,\n",
       "         98.3424423 ,   62.21530846,   33.20961827,   91.09063687,\n",
       "         92.64026196,   39.5253349 ,   60.36838334,   21.16142874,\n",
       "         56.59039101,   94.55164069,   86.244741  ,   72.55442657,\n",
       "         42.05279038,   67.93633005,    5.37151131,   99.04454912,\n",
       "         33.24887454,   87.2871225 ,   63.54412699,   96.60351822,\n",
       "         51.74265522,   40.86135687,   99.11271843,   43.64550656,\n",
       "         78.84702127,    0.31898426,   53.95309055,   80.04693308,\n",
       "         55.4543227 ,   48.01557931,   96.50512604,   29.66234664,\n",
       "         34.69708701,   71.87592507,   77.7374347 ,    5.29619788,\n",
       "         24.88866454,   93.00191573,   39.03444879,   99.5965773 ,\n",
       "         21.4668194 ,   37.5841322 ,   61.14813967,   74.96928091,\n",
       "         58.60563282,   17.92562535,   58.93484217,   51.86343098,\n",
       "         60.4328008 ,   36.27795209,   26.74026946,   41.20192523,\n",
       "         47.67800952,   21.81868475,   47.39105885,   21.59115845,\n",
       "         55.48690364,   64.08154875,   24.3096622 ,   55.59905327,\n",
       "         58.65349517,    8.6681083 ,   72.47454541,   78.59309153,\n",
       "         76.08674919,   68.57041693,    9.63761975,   92.40458943,\n",
       "         59.57571197,   96.67837817,   71.96735636,    2.80768188,\n",
       "          8.28718958,   78.84534061,    1.70297725,   16.46525959,\n",
       "         22.09532701,   50.34284126,   87.89352842,   54.12404637,\n",
       "       -100.        ,  180.        ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建实验数据\n",
    "data = np.random.rand(100) * 100\n",
    "data = np.append(data, np.array([-100, 180]))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b33d51f4-69fa-4526-ad22-bc6530d7aef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱线图\n",
    "plt.boxplot(data, sym=\"^\", labels=[\"Test\"], meanline=True, showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1a6b65-de38-46cd-920a-21bff96e4607",
   "metadata": {},
   "source": [
    "## 二、多个箱线图的绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28a78161-0470-4669-83c5-8cbd12f31206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取实验数据\n",
    "athletes = pd.read_csv(\"athletesAll.csv\")\n",
    "athletes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "123fc475-774b-4a41-b08e-62fba31137f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取各国身高数据\n",
    "countries = {\n",
    "    \"CHN\": \"中国\",\n",
    "    \"JPN\": \"日本\",\n",
    "    \"KOR\": \"韩国\",\n",
    "    \"USA\": \"美国\",\n",
    "    \"CAN\": \"加拿大\",\n",
    "    \"BRA\": \"巴西\",\n",
    "    \"GBR\": \"英国\",\n",
    "    \"FRA\": \"法国\",\n",
    "    \"ITA\": \"意大利\",\n",
    "    \"ETH\": \"埃塞俄比亚\",\n",
    "    \"KEN\": \"肯尼亚\",\n",
    "}\n",
    "\n",
    "data4draw = []\n",
    "for code in countries:\n",
    "    acountry = athletes[athletes[\"NOC\"] == code][\"Height\"].dropna()\n",
    "    data4draw.append(acountry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8268b2e1-3980-486c-8f4b-496743f6af2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置画板\n",
    "plt.figure(figsize=(12, 5), dpi=120)\n",
    "# 设置绘图数据\n",
    "plt.boxplot(data4draw)\n",
    "# 设置x轴标签\n",
    "plt.xticks(range(1, 12), countries.values(), fontproperties=font)\n",
    "# 设置title\n",
    "plt.title(\"各国运动员身高\", fontproperties=font)\n",
    "# 绘图\n",
    "plt.show()"
   ]
  }
 ],
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